ArticleComputational intelligence and neuroscience2021
A New Random Forest Algorithm Based on Learning Automata.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- EMCN is associated with vascular-immune crosstalk and represents a potential biomarker in lung adenocarcinoma.Frontiers in molecular biosciences · 2026Article
- A Smart System for Continuous Sitting Posture Monitoring, Assessment, and Personalized Feedback.Sensors (Basel, Switzerland) · 2025Article
- Metabolic profiles in laryngeal cancer defined two distinct molecular subtypes with divergent prognoses.Frontiers in immunology · 2025Article
- Artificial intelligence in traditional Chinese medicine: advances in multi-metabolite multi-target interaction modeling.Frontiers in pharmacology · 2025Review
- Identification of biomarkers for the diagnosis of chronic kidney disease (CKD) with dilated cardiomyopathy (DCM) by bioinformatics analysis and machine learning.Frontiers in genetics · 2025Article
- Identification of immune-related biomarkers linked to systemic lupus erythematosus and dilated cardiomyopathy through integrated bioinformatics analysis and multiple machine learning algorithms.Frontiers in immunology · 2025Article
- Establishment of a risk prediction model for olfactory disorders in patients with transnasal pituitary tumors by machine learning.Scientific reports · 2024Article
- Article
- Machine learning-based radiomics strategy for prediction of acquired EGFR T790M mutation following treatment with EGFR-TKI in NSCLC.Scientific reports · 2024Article
- Screening of pathologically significant diagnostic biomarkers in tears of thyroid eye disease based on bioinformatic analysis and machine learning.Frontiers in cell and developmental biology · 2024Article
- [Recent research on machine learning in the diagnosis and treatment of necrotizing enterocolitis in neonates].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2023Article
- Construction of diagnostic and prognostic models based on gene signatures of nasopharyngeal carcinoma by machine learning methods.Translational cancer research · 2023Article
- Machine learning in TCM with natural products and molecules: current status and future perspectives.Chinese medicine · 2023Review
- Construction of Osteosarcoma Diagnosis Model by Random Forest and Artificial Neural Network.Journal of personalized medicine · 2023Article
- Machine learning based gray-level co-occurrence matrix early warning system enables accurate detection of colorectal cancer pelvic bone metastases on MRI.Frontiers in oncology · 2023Article
- Analytical Comparison of Risk Prediction Models for the Onset of Macrosomia Based on Three Statistical Methods.Disease markers · 2022Article
- Exploration of Potential Biomarkers and Immune Landscape for Hepatoblastoma: Evidence from Machine Learning Algorithm.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Feature Genes in Neuroblastoma Distinguishing High-Risk and Non-high-Risk Neuroblastoma Patients: Development and Validation Combining Random Forest With Artificial Neural Network.Frontiers in medicine · 2022Article
- LASSO-based machine learning models for the prediction of central lymph node metastasis in clinically negative patients with papillary thyroid carcinoma.Frontiers in endocrinology · 2022Article
- Deep Learning-Based Classification of Spoken English Digits.Computational intelligence and neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.